---
title: "Water Access Points in Africa"
author: "Antony Rono"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
source_code: embed
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
library(flexdashboard)
library(tidyverse)
library(tidytuesdayR)
library(scales)
library(lubridate)
library(magrittr)
library(plotly)
library(ggthemes)
library(ggmap)
library(gganimate)
library(highcharter)
library(maps)
library(ggraph)
theme_set(theme_light())
```
```{r Load Data, include=FALSE}
data <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-05-04/water.csv')
```
```{r Clean data, include=FALSE}
water <- data %>%
mutate(report_date = mdy(report_date),
install_year = ifelse(install_year > year(today()), NA_real_, install_year),
status_id = case_when(
status_id == "y" ~ "Working",
status_id == "n" ~ "Not Working",
status_id == "u" ~ "Unknown"
)
) %>%
separate(water_tech, into = c("water_tech", "brand"), sep = " - ", fill = "right")
## Retaining African Countries
countries <- get_data_from_map(download_map_data("https://code.highcharts.com/mapdata/custom/africa.js"))
water %<>%
mutate(country_name = case_when(
country_name == "Congo - Kinshasa" ~ "Democratic Republic of the Congo",
country_name == "Congo - Brazzaville" ~ "Republic of Congo",
country_name == "Tanzania" ~ "United Republic of Tanzania",
TRUE ~ country_name
)
) %>%
filter(country_name %in% countries$name)
```
```{r Function for breakdown by category, include=FALSE}
bar_chart_by_category <- function(x){
water %>%
filter(!is.na({{x}})) %>%
mutate({{x}} := fct_lump_n({{x}}, 20)) %>%
count({{x}}, sort = TRUE, name = "count") %>%
mutate({{x}} := fct_reorder({{x}}, count)) %>%
filter(count > 0, !is.na({{x}})) %>%
ggplot(aes(count, {{x}}, fill = {{x}})) +
geom_col() +
scale_x_continuous(expand = c(0,0), labels = comma)+
theme(legend.position = "none")
}
```
Overall {data-icon="fa-chart-pie"}
=======================================================================
Row {data-height=250}
-----------------------------------------------------------------------
### Total Water Access Points
```{r count of released}
valueBox(value = comma(nrow(water)), caption = "Total Water Acess Points Records", icon = "fa-user-tint", color = "green")
```
### Status of the Water Access Points
```{r water status}
water %>%
count(status_id) %>%
plot_ly(labels = ~ status_id, values = ~ n) %>%
add_pie(hole = 0.6)
```
### Facility Type of the Water Access Points
```{r water facility type}
water %>%
mutate(facility_type = ifelse(is.na(facility_type), "Unknown", facility_type)) %>%
count(facility_type) %>%
plot_ly(labels = ~ facility_type, values = ~ n) %>%
add_pie(hole = 0.6)
```
Row {data-height=750}
-----------------------------------------------------------------------
### Water Access Points by Country
```{r Map of country by Water Acess Pointss}
water_countries <- water %>%
filter(!is.na(country_name)) %>%
mutate(country_name = case_when(
str_detect(country_name, "Tanzania") ~ "Tanzania, United Republic of",
TRUE ~ country_name
)) %>%
mutate(`iso-a3`= coalesce(iso3166$a3[match(country_name,iso3166$ISOname)],
iso3166$a3[match(country_name ,iso3166$sovereignty)]))
hcmap(
map = "custom/africa", # high resolution world map
data = water_countries %>% count(`iso-a3`), # name of dataset
joinBy = "iso-a3",
value = "n",
showInLegend = FALSE, # hide legend
nullColor = "#FFFFFF",
download_map_data = TRUE,
dataLabels = list(enabled = TRUE, format = '{point.name}')
) %>%
hc_colorAxis(minColor = "#FFFFCC", maxColor = "red") %>%
hc_mapNavigation(enabled = FALSE) %>%
hc_legend("none") #%>%
#hc_title(text = "Production By Country") # title
```
### Water Access Points by Country and Type
```{r Breakdown by Country and Water Acess Pointss}
p <- water %>%
count(country_name, water_source, sort = TRUE, name = "count") %>%
group_by(country_name) %>%
mutate(total = sum(count, na.rm = TRUE)) %>%
ungroup() %>%
mutate(country_name = fct_reorder(country_name, total)) %>%
filter(count > 0, !is.na(country_name)) %>%
ggplot(aes(count, country_name, fill = water_source)) +
geom_col() +
scale_x_continuous(expand = c(0,0), labels = comma)+
theme(legend.position = "right")
ggplotly(p)
```
Breakdowns and Trends {data-icon="fa-chart-line"}
=======================================================================
Row {data-height=650}
-----------------------------------------------------------------------
### Distribution of Water Access Points
```{r Records by Water Acess Points}
ggplotly(bar_chart_by_category(water_source))
```
### Distribution of Technologies Used in the Access Points
```{r Water Records by Tech}
ggplotly(bar_chart_by_category(water_tech))
```
### Distribution of Water Access Points Installers
```{r Records by Installer}
ggplotly(bar_chart_by_category(installer))
```
Row {data-height=350}
-----------------------------------------------------------------------
### Trend of Water Access Points Installations
```{r Trend of Water Acess Points install year}
p <- water %>%
count(install_year, water_source, name = "count") %>%
group_by(install_year) %>%
mutate(total = sum(count)) %>%
filter(!is.na(install_year), install_year > 1970, !is.na(water_source)) %>%
ggplot(aes(install_year, count, color = water_source, text = water_source)) +
geom_point() +
geom_line() +
geom_point(aes(install_year, total, color = "Total",text = "Total"), color = "brown",inherit.aes = FALSE)+
geom_line(aes(install_year, total, color = "Total",text = "Total"),color = "brown", inherit.aes = FALSE)+
scale_y_continuous( labels = comma)+
theme(legend.position = "bottom")
ggplotly(p, tooltip = c("x","y", "text"))
```
Relationships {data-orientation=columns data-icon="fa-chart-bar"}
=======================================================================
Column {.tabset}
-----------------------------------------------------------------------
### Water Tech Vs Water Access Points - Baloon Chart
```{r Most common tech by Water Acess Points - Baloon chart}
library(ggpubr)
tech_source_edges <- water %>%
count(water_source, water_tech, sort = TRUE, name = "count") %>%
filter(!is.na(water_source), !is.na(water_tech)) %>%
rename(to = water_tech,
from = water_source) %>%
filter(count > 1)
tech_source_edges %>%
pivot_wider( names_from = from, values_from = count, values_fill =0) %>%
column_to_rownames(var="to") %>%
ggballoonplot(., fill = "value") +
scale_fill_viridis_c(option = "C") +
coord_flip()
# tech_source_edges %>%
#
# ggballoonplot(x = "to", y = "from", size = "count", fill = "count") +
#
# scale_fill_viridis_c(option = "C")
```
### Water Tech Vs Water Water Access Points - Network Graph
```{r Most common tech by Waster source - Network Graph}
library(widyr)
library(igraph)
library(threejs)
tech_source_vertices <- tech_source_edges %>%
select(-count) %>%
pivot_longer(to:from, values_to = "text") %>%
distinct(text) %>%
mutate(type = ifelse(text %in% tech_source_edges$from, "Water Acess Points", "water_tech"),
type = as.factor(type))
g <- graph_from_data_frame(d = tech_source_edges, directed = FALSE, vertices = tech_source_vertices)
V(g)$color = ifelse(V(g)$type == "Water Acess Points", "orange", "dodgerblue")
plot(g,
layout = layout_nicely(g),
vertex.label.color = "black",
vertex.size = 20*eigen_centrality(g)$vector,
vertex.label.cex = 0.5,
)
# graphjs(g,
#
# layout = layout_randomly(g, dim = 3),
# #vertex.size = 25*eigen_centrality(g)$vector,
#
# vertex.label = "x",
#
# vertex.label.cex = 0.8
#
# )
#
#
```
Column
-----------------------------------------------------------------------
### Install Date Vs Report Date
```{r Relationship between Report Date and Installed Date}
water %>%
mutate(year = year(report_date)) %>%
filter(!is.na(install_year), install_year > 1900, year(report_date) > 1999) %>%
ggplot(aes(report_date, install_year)) +
geom_point() +
coord_flip()
```
